EDA for part 3 alternative fuel charging station in the US

import pandas as pd
import numpy as np

df = pd.read_csv("data/alt_fuel_stations (Mar 15 2024).csv")

df.head(10)

/var/folders/fd/glpyb_f16c52ys5wz5cgrb5r0000gn/T/ipykernel_8260/1673416687.py:4: DtypeWarning:

Columns (6,16,20,31,33,39,40,41,43,46,52,55,57,58,60,62,65,67,69,71) have mixed types. Specify dtype option on import or set low_memory=False.

	Fuel Type Code	Station Name	Street Address	Intersection Directions	City	State	ZIP	Plus4	Station Phone	Status Code	...	Restricted Access	RD Blends	RD Blends (French)	RD Blended with Biodiesel	RD Maximum Biodiesel Level	NPS Unit Name	CNG Station Sells Renewable Natural Gas	LNG Station Sells Renewable Natural Gas	Maximum Vehicle Class	EV Workplace Charging
0	CNG	Spire - Montgomery Operations Center	2951 Chestnut St	NaN	Montgomery	AL	36107	NaN	NaN	E	...	NaN	NaN	NaN	NaN	NaN	NaN	False	NaN	MD	NaN
1	CNG	Metropolitan Atlanta Rapid Transit Authority	2424 Piedmont Rd NE	NaN	Atlanta	GA	30324	NaN	NaN	E	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	LD	NaN
2	CNG	United Parcel Service	270 Marvin Miller Dr	NaN	Atlanta	GA	30336	NaN	NaN	E	...	NaN	NaN	NaN	NaN	NaN	NaN	True	NaN	HD	NaN
3	CNG	Arkansas Oklahoma Gas Corp	2100 S Waldron Rd	NaN	Fort Smith	AR	72903	NaN	479-783-3181	E	...	False	NaN	NaN	NaN	NaN	NaN	False	NaN	MD	NaN
4	CNG	Clean Energy - Logan International Airport	1000 Cottage St Ext	From Route 1, take the first exit after Callah...	East Boston	MA	2128	NaN	866-809-4869	E	...	False	NaN	NaN	NaN	NaN	NaN	True	NaN	MD	NaN
5	CNG	Clean Energy - Everett - National Grid	16 Rover St	Rt 16, exit to Rt 99, to Dexter St to Rover. O...	Everett	MA	2149	NaN	866-809-4869	E	...	False	NaN	NaN	NaN	NaN	NaN	False	NaN	HD	NaN
6	CNG	Clean Energy - Greenpoint - National Grid	287 Maspeth Ave	I-278/Brooklyn Queens Expy, exit onto Vandervo...	Brooklyn	NY	11211	NaN	866-809-4869	E	...	False	NaN	NaN	NaN	NaN	NaN	True	NaN	HD	NaN
7	CNG	Canarsie - National Grid	8424 Ditmas Ave	From Shore Pkwy, take Rockaway Pkwy N, left on...	Brooklyn	NY	11236	NaN	866-809-4869	E	...	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MD	NaN
8	CNG	Con Edison - Van Nest Service Center	1615 Bronxdale Ave	Hutchinson River Parkway, exit onto E Tremont ...	Bronx	NY	10462	NaN	718-904-4504	E	...	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MD	NaN
9	CNG	Con Edison - Rye Service Center	178 Theodore Fremd Ave	I-95/New England Thruway, exit 19 onto Playlan...	Rye	NY	10580	NaN	914-933-2910	E	...	False	NaN	NaN	NaN	NaN	NaN	NaN	NaN	MD	NaN

10 rows × 74 columns

ELEC: Electricity. This fuel type is used by electric vehicles (EVs), which are powered by electric motors and battery packs, without the use of traditional internal combustion engines.

E85: Ethanol Fuel Blend (85% ethanol, 15% gasoline). E85 is a biofuel blend of 85% denatured ethanol fuel and 15% gasoline or other hydrocarbons by volume. It’s used in flexible-fuel vehicles which can operate on gasoline or ethanol blends varying from E85 to E0 (all gasoline).

LPG: Liquefied Petroleum Gas. Also known as propane or butane, LPG is a clean-burning fossil fuel used by some vehicles either as a primary fuel or as an alternative to gasoline. It’s stored in a liquid state under pressure in fuel tanks but becomes gas when released.

BD: Biodiesel. Biodiesel is a renewable, biodegradable fuel manufactured domestically from vegetable oils, animal fats, or recycled restaurant grease. It’s a cleaner-burning replacement for petroleum diesel fuel.

CNG: Compressed Natural Gas. CNG is a fossil fuel substitute for gasoline, diesel, or propane fuel. It’s much cleaner than those fuels and is used in some cars that are specifically designed or modified to run on CNG.

RD: Renewable Diesel. Also known as green diesel, renewable diesel is made from renewable resources like vegetable oils and animal fats. It’s chemically similar to petroleum diesel but emits less pollution.

LNG: Liquefied Natural Gas. LNG is natural gas that has been cooled down to liquid form for ease and safety of non-pressurized storage or transport. It’s used in vehicles specially equipped to handle LNG, offering lower emissions than traditional diesel.

HY: Hydrogen. This refers to hydrogen fuel cell vehicles which use hydrogen gas to power an electric motor. They emit only water vapor and warm air, making them one of the cleanest fuel options available.

df['Fuel Type Code'].value_counts()

Fuel Type Code
ELEC    84148
E85      4529
LPG      3521
BD       1702
CNG      1513
RD        611
LNG       125
HY        125
Name: count, dtype: int64

Histogram with drop-down bar for each state.

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd

states = df['State'].unique()

# Create a figure
fig = go.Figure()

# Add one trace (bar) for each state to the figure
for state in states:
    fig.add_trace(
        go.Histogram(
            x=df[df['State'] == state]['Fuel Type Code'],
            name=state,  # This will be the label in the dropdown
            visible=(state == states[0])  # Only the first state is visible initially
        )
    )

# Create dropdown menus
dropdown_buttons = [
    {'label': state,
     'method': 'update',
     'args': [{'visible': [s == state for s in states]},
              {'title': f'Histogram of Values for {state}'}]}  # This changes the figure title
    for state in states]

# Add dropdown to the figure
fig.update_layout(
    updatemenus=[{'buttons': dropdown_buttons,
                  'direction': 'down',
                  'showactive': True,}]
)

# Show the figure
iplot(fig)

Horizontal Stacked histogram for fuel type for each state.

states = df['State'].unique()
fuel_types = df['Fuel Type Code'].unique()

# Initialize an empty list for the traces
traces = []

# Create a trace for each fuel type
for fuel_type in fuel_types:
    # Initialize a list to hold the counts for each state
    counts = []
    for state in states:
        # Count the occurrences of the fuel type in the current state
        count = df[(df['State'] == state) & (df['Fuel Type Code'] == fuel_type)].shape[0]
        counts.append(count)
    
    # Create a horizontal bar trace for the current fuel type
    traces.append(go.Bar(
        y=states,  # Swap x and y
        x=counts,  # Swap x and y
        name=fuel_type,
        orientation='h'  # Specify horizontal orientation
    ))

# Create the figure and update layout for stacked histogram (bar chart)
fig = go.Figure(data=traces)
fig.update_layout(
    barmode='stack',
    title_text='Stacked Histogram of Fuel Types by State',  # Chart title
    yaxis_title_text='State',  
    xaxis_title_text='Frequency', 
    height = 1000,
    width = 800
)

# Show the plot
fig.show()

Geospatial Dataset:

import geopandas as gpd
import pandas as pd
import plotly.express as px

geojson_path = 'data/alt_fuel_stations (Mar 15 2024).geojson'
gdf = gpd.read_file(geojson_path)

state_col_name = 'state'  
fuel_type_col_name = 'fuel_type_code'  

# Aggregate data by 'State' and 'Fuel Type Code'
aggregated_data = gdf.groupby([state_col_name, fuel_type_col_name]).size().reset_index(name='Count')

fig = px.scatter_geo(aggregated_data,
                     locations=state_col_name,  
                     locationmode="USA-states",
                     color=fuel_type_col_name,  
                     size="Count",  
                     hover_name=fuel_type_col_name,  
                     hover_data={state_col_name: True, "Count": True},  
                     scope="usa", 
                     title="Fuel Type Code Counts by State")

fig.show()

As scattered points

df = pd.DataFrame(gdf.drop(columns='geometry'))
df['longitude'] = gdf.geometry.x
df['latitude'] = gdf.geometry.y

fuel_type_col = 'fuel_type_code'

fig = px.scatter_geo(df,
                     lon='longitude',
                     lat='latitude',
                     color=fuel_type_col, 
                     hover_name=fuel_type_col, 
                     scope='world',  
                     title='Fuel Stations by Fuel Type across the USA')

fig.update_traces(marker=dict(size=2))

fig.update_layout(
    height = 1000,
    width = 800
)

fig.update_geos(
    projection_type="albers usa",  
    landcolor="lightgrey",
    lakecolor="white",
    showocean=True, oceancolor="azure")

fig.show()

As heat map

import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
import pandas as pd

geojson_path = 'data/alt_fuel_stations (Mar 15 2024).geojson'
df = pd.DataFrame(gdf.drop(columns='geometry'))
df['longitude'] = gdf.geometry.x
df['latitude'] = gdf.geometry.y
df_aggregated = df.groupby(['fuel_type_code', 'latitude', 'longitude']).size().reset_index(name='Count')

fuel_types = df_aggregated['fuel_type_code'].unique()

# Create a figure
fig = go.Figure()

for fuel_type in fuel_types:
    df_filtered = df_aggregated[df_aggregated['fuel_type_code'] == fuel_type]
    
    # Create a heatmap for each fuel type
    fig.add_trace(
        go.Densitymapbox(lat=df_filtered['latitude'], lon=df_filtered['longitude'],
                         z=df_filtered['Count'], name=fuel_type, visible=False)
    )

# Make the first fuel type visible by default
fig.data[0].visible = True

# Set mapbox style
fig.update_layout(mapbox_style="light", mapbox_center_lon=180)
fig.update_layout(mapbox=dict(center=dict(lat=38, lon=-94), zoom=1))

# Create dropdown buttons
buttons = []

for i, fuel_type in enumerate(fuel_types):
    button = dict(
        label=fuel_type,
        method="update",
        args=[{"visible": [False] * len(fuel_types)},
              {"title": f"Heatmap of {fuel_type} Fuel Stations"}])
    button["args"][0]["visible"][i] = True  
    buttons.append(button)

fig.update_layout(
    updatemenus=[
        dict(
            buttons=buttons,
            direction="down",
            pad={"r": 10, "t": 10},
            showactive=True,
            x=0.1,
            xanchor="left",
            y=1.1,
            yanchor="top"
        ),
    ]
)

# Note: To use mapbox, you may need to set a Mapbox access token
# Set your Mapbox token here
mapbox_access_token = 'pk.eyJ1IjoiY2F0aHl6d24iLCJhIjoiY2x1a2VnN2FjMDNhdzJpbGw5Y3Jud2Q5MyJ9.bjGv9ATrYWnL4B66WwCH7g'
fig.update_layout(mapbox_accesstoken=mapbox_access_token)

fig.show()